CV Lattes
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Universidade de São Paulo (USP). Instituto de Matemática e Estatística (IME) (Institutional affiliation from the last research proposal) Birthplace: Brazil
bachelor's at Bacharelado in Phisics from Universidade de São Paulo (1984), master's at Computer Science from Universidade de São Paulo (1986) and doctorate at Electric Engineering from Universidade de São Paulo (1998). She is currently an associated professor (MS5) at Universidade de São Paulo. Has experience in Computer Science, focusing on Artficial Intelligence, acting on the following subjects: Automated Planning, Planning under Uncertainty, AI in Education and Model Based Diagnosis. (Source: Lattes Curriculum)
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The GDP of the state of São Paulo reached R$ 2.38 trillion in 2019, with a 2.5% increase compared to 2018. Associated with this growth, there is a drop in some crime indicators, supported by the use of a semi-automated image monitoring system. However, there are still significant demands for the intelligent automation of the live video monitoring process in the areas of public security, i…
(Only some records are available in English at this moment)
In the field of probabilistic planning, in order to efficiently solve sequential decision-making problems modeled by Markov Decision Processes (MDPs), it is crucial to handle stochastic knowledge defined over complex relations that may involve features of transitivity, symmetry, recursion, determinism and context-specific independence. However, traditional representational formalisms used…
Probabilistic planning is a general framework to represent decision problems in which actions have probabilistic effects. Due to the importance of such framework, it has being studied in several different areas, e.g., operations research, control theory, economics and robotics. Different from other areas, artificial intelligence has focused on domain-independent probabilistic planning, t…
Probabilistic planning problems are often modeled as Markov Decision Processes (MDPs). The Solution to one such problem is an optimal policy, i.e., a mapping from states to actions that will bring the greatest estimated reward for an agent that follows it. A well known class of algorithms in the case that the initial state and the goals are known is based on Real Time Dynamic Programming …
(Only some records are available in English at this moment)
This research internship aims at further developing the proponent's PhD thesis on "Markov Decision Processes specified through Probabilistic Logic Programming" by extending its representational formalism to the realm of mixed continuous and discrete planning problems, as well as by investigating efficient algorithmic solutions based on symbolic reasoning and deep neural models. Given rece…
Many probabilistic planning problems can be modelled by a discrete and continuous Markov decision process (DC-MDP), which is a general version of the Markov decision process (MDP). Since solutions for DC-MDPs are few in literature and not general, we intend to propose a new solution based on the efficient solutions for MDPs. Two efficient sample based algorithms are: (i) Real Time Dynamic…
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| 8 / 6 | Completed scholarships in Brazil |
| 2 / 2 | Completed scholarships abroad |
| 16 / 13 | All research grants and scholarships |
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